How Daloopa is transforming fundamental data for equity analysts

Today we announce our Series B funding round! Daloopa CEO Thomas Li takes over the blog to share his views on this exciting milestone for our company.

May 7, 2024

I almost wasn’t going to write about our Series B. Not because it isn't an important and exciting milestone for Daloopa, but because announcements tend to be about the valuation and dollars raised, rather than on the company, what we're solving for, and why we are seeing success. So, I wanted to take this opportunity to share my own story, how Daloopa came about, and what problems we solve.

The beginnings

Before starting this journey, I worked as an analyst on the buy-side focused on long/short equity. Every earnings season, I faced the grueling task of updating models—plugging numbers from quarterly reports, 8Ks, and investor decks into my Excel models. It needed to be done quickly and accurately so that I could start my actual job of analysis and understanding the landscape of my space. Every time I found myself having to plug in new numbers, I kept having to spend time auditing it, plugging any data gaps and checking for any errors. The constant data entry and error checking is a cycle that is too common in investment services. So common, in fact, that we accept that it is a part of our jobs, when it really shouldn’t be.  

This recurring challenge is what led me to start Daloopa with my two co-founders five years ago. We set out to build a product that would allow financial analysts across the Street to easily update their models in close to real time, with extreme accuracy so they could focus on being an analyst.

The industry needs a different approach to fundamental data discovery

We knew to truly make an impact in this industry, we had to do things differently. I was not only astounded by how manual this work still is; I also saw that existing solutions were merely perpetuating the problem by being an outsourced version of the manual work many were doing in-house.

Some will say “Who cares how the data gets to me as long as I get it?” But this is exactly what’s holding the industry back. Manual work – no matter if it’s in-house by the analyst or outsourced to someone else – limits an analyst’s ability to get the most complete, the most accurate set of data on a company in rapid speed. It’s just not humanly possible to do all three things at once manually.  What we need in financial services is to build a car, not just a faster horse.

That’s why we decided to build Daloopa from ground up with a tech-first approach, using AI as a catalyst. We chose AI not for the hype (when we started the company, there was none) but because it is simply the best tool for solving these complex problems for our customers. It automates the sourcing, organizing, and seamless delivery of data—and it does this exceptionally well. And what’s even better, it’s continuously learning and improving. Since we started this five years ago, our algorithms are highly trained and continue to be ahead of the curve in making improvements for customers.  

What is Daloopa?

Daloopa is a simple business. We offer the cleanest and deepest database of historical, fundamental financials for public companies. We take all the data from filings—including investor presentations, footnotes, KPIs, and operating data—and, with the help of AI, integrate them into a database. Our customers can access this data in two convenient ways:

  • Daloopa Data sheet: By simply downloading our datasheet from the Daloopa marketplace, you get all the historical spread on your coverage—from every KPI adjustment and GAAP to non-GAAP operating data, to guidance—organized in a single Excel file.
Daloopa Marketplace: Data sheet download options

  • Daloopa Add-in: With one click, you can update your model when a company reports earnings. Just click a button, and your entire model is updated—down to every adjustment—in your format, in your style, without the need for editing or using formulas.
Daloopa Add-in: Update with the click of a button

What are the main use cases for Daloopa?

Our tools help analysts' eliminate backlog, manage updates for multiple companies during those critical first hours after earnings releases, and aid in quickly ramping up and initiating coverage. You no longer have to be in a position where updating your model is a solo effort. We eliminate the monkey work so analysts can focus on the essential tasks.

Our coverage is extensive—we have one of the largest databases of public companies available today, and we continue to expand our reach daily. With AI technology continuously improving our processes, we are able to increase the speed of updates and accelerate the time to insights.

Celebrating our Series B: Accelerating our mission to make fundamental data better

Today, I’m very excited to announce Daloopa’s Series B funding round. (You can read the official press release here.) This milestone is not merely about capital; it’s about acceleration. Our investors have shown a deep belief in our vision, and this new funding will enable us to innovate faster and expand our reach.

This Series B is about solving the problems of delivering quality data in a new way—the Daloopa Way—through an AI-powered fundamental and historical data infrastructure that provides the most complete, accurate and fast data directly into analysts’ models with a click of a button. With this investment, we will enhance our product, our coverage and expand our reach.

We’ve been working on Daloopa since 2019—applying our learnings, reinvesting in our product, making Daloopa the most complete, accurate, and fast provider of historical data. We are continuing to build the infrastructure to fuel your research process and deliver insights that drive performance.

We are excited about this next phase of our journey, and we are more committed than ever to revolutionizing how financial data is managed. Thank you for being part of our story.

Thomas Li  
CEO Daloopa

See it for yourself

Super Charging Financial Models with AI

Use the deepest and most accurate set of public company historicals to build your models in your preferred method, and update existing models in any formatting.